FraudGuard - Real-time Fraud Detection System

A comprehensive fraud detection system with real-time monitoring, machine learning-based predictions, and interactive visualizations.

Features

• Real-time fraud detection using machine learning
• Interactive dashboard with multiple visualizations
• Transaction monitoring and analysis
• Risk assessment and scoring
• User authentication and role-based access
• API endpoints for integration

Tech Stack

Frontend

• React.js
• Material-UI
• Recharts for visualizations
• Axios for API calls

Backend

• FastAPI
• Python
• Scikit-learn
• Pandas
• NumPy

Project Structure

fraud_detection/
├── backend/
│   ├── app/
│   │   ├── router.py
│   │   └── models/
│   ├── utils/
│   ├── config.py
│   └── main.py
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── services/
│   │   └── utils/
│   └── public/
└── README.md

Getting Started

Prerequisites

• Python 3.8+
• Node.js 14+
• npm or yarn

Backend Setup

1. Create a virtual environment:

   cd backend
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

   pip install -r requirements.txt

3. Start the backend server:

   python -m uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Frontend Setup

1. Install dependencies:

   cd frontend
   npm install

2. Start the development server:

   npm start

The application will be available at:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000

API Endpoints

• GET /api/v1/status - API status check
• POST /api/v1/predict - Fraud prediction endpoint
• GET /api/v1/dashboard/summary - Dashboard summary data
• GET /api/v1/dashboard/fraud-distribution - Fraud distribution data
• GET /api/v1/dashboard/model-performance - Model performance metrics

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Material-UI for the component library
• Recharts for the visualization library
• FastAPI for the backend framework

🚀 Features

• Dataset Generation: 50,000 synthetic transactions with 10 relevant features
• Multiple ML Models: Implementation of 4 powerful algorithms
  • Random Forest
  • XGBoost
  • LightGBM
  • CatBoost
• Advanced Visualizations: 8 different types of data visualizations
• Class Imbalance Handling: SMOTE technique implementation
• Comprehensive Evaluation: Multiple performance metrics

📊 Project Structure

fraud_detection/
├── data/               # Dataset directory
├── docs/              # Documentation and visualizations
├── models/            # Trained models
├── notebooks/         # Jupyter notebooks
├── src/              # Source code
└── tests/            # Unit tests

🛠️ Technical Stack

• Languages: Python 3.x
• ML Libraries:
  • Scikit-learn
  • XGBoost
  • LightGBM
  • CatBoost
• Data Processing:
  • Pandas
  • NumPy
• Visualization:
  • Matplotlib
  • Seaborn
  • Plotly
• Development:
  • Jupyter Notebooks
  • Git

🚀 Getting Started

1. Clone the repository

git clone https://github.com/Arindam-GitH/Payment-Fraud-Detection-.git
cd Payment-Fraud-Detection-

2. Set up virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the analysis

python src/main.py

📈 Model Performance

Each model is evaluated using:

• Accuracy
• Precision
• Recall
• F1-Score
• ROC-AUC Score

📊 Visualizations

The project includes 8 different types of visualizations:

1. Transaction Amount Distribution
2. Fraud vs Non-Fraud Distribution
3. Correlation Heatmap
4. Transaction Time Analysis
5. Amount vs Fraud Box Plot
6. Customer Age Distribution
7. Device Type Distribution
8. Geographic Distribution

🤝 Contributing

Feel free to submit issues and enhancement requests!

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Arindam Guha

• GitHub: @Arindam-GitH

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⭐️ From Arindam-GitH